Dynamic Sourcing Intelligent Cubes Database Report Speed
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Solution Overview
Problem
Current database systems face inefficiencies in report generation due to the need to access underlying databases for data retrieval, which can slow down the process and require extensive processing resources.
Innovation Solution
Implementing intelligent cubes as pre-aggregated, pre-calculated caches within the database system that allow for dynamic selection and use of pre-processed data, enabling faster report generation by accessing pre-processed data from cubes instead of the underlying database, and allowing for additional filtering, aggregation, and calculations without direct access to the database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-processed data is stored in intelligent cubes, then report generation speed is improved, but system complexity increases
Solution Approach 1:
The system performs data aggregation and calculation in advance by storing pre-computed results in intelligent cubes. When a report request arrives, the system retrieves pre-calculated data from cubes rather than computing from raw data, significantly reducing report generation time while managing complexity through automated cube management
Solution Approach 2:
Intelligent cubes serve as an intermediary layer between the raw database and the report generation system. The cubes pre-process and store aggregated data, acting as a buffer that speeds up report generation without requiring direct access to the underlying database for every report query
2Loss of time
If data is retrieved from the underlying database, then data accuracy is ensured, but processing time increases
Solution Approach 1:
Data is pre-processed and aggregated into intelligent cubes in advance, storing calculated results that can be quickly retrieved. This preliminary computation ensures that when reports are generated, the system uses pre-validated data from cubes, maintaining accuracy while reducing retrieval time
Solution Approach 2:
The system creates copies of processed data in intelligent cubes that mirror the structure and accuracy of the underlying database. These cube copies contain pre-computed aggregated data that can be retrieved quickly while maintaining the same data integrity and accuracy standards as the source database
3Productivity
If pre-processed data is used from cubes, then processing efficiency is improved, but data freshness may be compromised
Solution Approach 1:
The intelligent cube system dynamically determines whether to use pre-processed data from cubes or retrieve fresh data from the underlying database based on the specific report requirements. This dynamic approach allows the system to leverage pre-processed data for efficiency when appropriate while maintaining the option to access fresh data when needed
Solution Approach 2:
The system changes the parameter of data source selection based on report requirements. For reports that can tolerate slight delays, pre-processed cube data is used for high efficiency. For reports requiring the latest data, the system switches to retrieving fresh data from the underlying database, optimizing the trade-off between efficiency and freshness
Data Source
AI summary
Dynamic sourcing, in which a data request that is associated with a query is received and a parameter of data needed for satisfaction of the query is identified. Parameter information defining data available in at least one cube stored in a cache is accessed and the parameter is compared with the parameter information. Based on comparison results, it is determined whether one or more cubes in the cache include sufficient data to satisfy the query. In response to a determination that one or more cubes include sufficient data to satisfy the query, a response to the data request is generated by executing the query against the one or more cubes. In response to a determination that the cubes do not include sufficient data to satisfy the query, a response to the data request is generated by executing at least a portion of the query against a database system.